3 answers
3
Asked 801 views

How do I become a data scientist? Should I join Kaggle?

How many people here are on the site because you're doing the Kaggle competition? Curious as to what cities you're from and how your profession was impacted by your college major. #college #college-major #college-major #science #undecided

college-major science undecided
Answer This

+25 Karma if successful
From: You
To: Friend
Subject: Career question for you
3

3 answers

0
Updated
Share a link to this answer
Share a link to this answer

Nicole’s Answer

Hi Candace C. I see that you posted this question a little while ago so I hope my answer to you (or others who may read this response) is still helpful.

I actually learned quite a bit both from your question and the answers provided. Sharing a slightly different perspective on becoming a data scientist.

Data science is an area that I sort of drifted into after having worked in data management, coding and wrangling for many years. So my path in this regard is just an example of a person that can/does work in the area of data science though I have an engineering degree. Also important to share that there are important elements of data science that you can learn through certification programs. That is also a path that I took as I gained interest in the field.

In short, I hope you know/learn that there are many paths to "becoming" a skilled worker and that those paths can include jumping right in during your college and early work years...or later on in your career when you find new technologies for which you may have some interest.

Best of luck to you in your journey!
0
0
Updated
Share a link to this answer
Share a link to this answer

Sri Athithya Kruth’s Answer

Data Science is a very diverse and interesting field. There are many sub domains in data science including:

  • Machine Learning
  • Deep Learning
  • Database Management Systems (SQL, noSQL based ones)
  • Data cleaning, wrangling etc.

You can do this through numerous courses available online.

For Machine learning, you can try Andrew Ng's famous Machine Learning course on Coursera. This course is famous as it covers Machine Learning in decent depth, and teaches you the math behind the algorithms, which is very important.

There are many good courses for Data Science on sites like Datacamp, Udacity etc. I suggest going through the curriculum for some of these, and trying to learn some of the content by yourself. If it interests you, you can sign up for one of these and continue learning.

Signing up for Kaggle is also a good idea. Kaggle has free courses for all of the topics you would need to know to compete in the contests. Going through these courses would help you get a good start on Kaggle as these are designed for people interested in Kaggle competitions.

Sri Athithya Kruth recommends the following next steps:

Try going through Andrew Ng's free Machine learning course on Coursera.
Sign up for Kaggle, and start taking some of their free courses to start up on Kaggle contests.
Once you think you have a good grasp of the basic concepts on Kaggle, try the Titanic Survival Problem. There are several Kernels (these are solutions to the problem posted by people on the site) you can refer to in case you would like to look at possible solutions.
0
0
Updated
Share a link to this answer
Share a link to this answer

Mesut’s Answer

First of all you, shold be sure that you know what the data science is and the differences with other diciplines of domain. While studying on Data Science, AI, Data Mining or machine learning, after getting several courses and finishing several books, trying to find solution to real life problem is the most effective way of learning and factorizing yoir knowledge. And yes kaggle is the right place to do that, because besides implementing solution, you can communicate with others and learn new approches.

Mesut recommends the following next steps:

Make a research on searh engines to see the differences of data mining, data science, machine learning and artificial intelligence in all ways (i.e. salary, carrier, leaening curve, academic researhes, community support, technological support, etc ) (Do not expect huge differences in terms of material)
Once you've capted the diff, select the one you want to implement
Start reading several blog posts like intro to datascience, data science for beginners
Take several formations on online formation areas(udscity, coursera, etc.). There arw also really good free ones.
Yes, join kaggle competitions. As the prpblem statement and the data are ready, you focus on solution, communicate woth others, inspire from other kernels. This gives you a really good understanding of different techniques, especially at the beginning. And if possible try to find a solution for a real lofe problem around you (school, internship, work, etc.)
0